Related references
Note: Only part of the references are listed.Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction
Madison Wenzlick et al.
JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE (2021)
A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
Osman Mamun et al.
SCIENTIFIC REPORTS (2021)
Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning
Yue Liu et al.
ACTA MATERIALIA (2020)
A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
Osman Mamun et al.
NPJ COMPUTATIONAL MATERIALS (2020)
Modern data analytics approach to predict creep of high-temperature alloys
D. Shin et al.
ACTA MATERIALIA (2019)
Data-Driven Materials Science: Status, Challenges, and Perspectives
Lauri Himanen et al.
ADVANCED SCIENCE (2019)
Catalog of NIMS creep data sheets
Kota Sawada et al.
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (2019)
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin
NATURE MACHINE INTELLIGENCE (2019)
Machine learning and data science in soft materials engineering
Andrew L. Ferguson
JOURNAL OF PHYSICS-CONDENSED MATTER (2018)
Inverse design in search of materials with target functionalities
Alex Zunger
NATURE REVIEWS CHEMISTRY (2018)
Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling et al.
SCIENCE (2018)
Basic modelling of creep rupture in austenitic stainless steels
Junjing He et al.
THEORETICAL AND APPLIED FRACTURE MECHANICS (2017)
An intermediate temperature creep model for Ni-based superalloys
Young-Kwang Kim et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2016)
A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
Maarten de Jong et al.
SCIENTIFIC REPORTS (2016)
Perspective: Materials informatics and big data: Realization of the fourth paradigm of science in materials science
Ankit Agrawal et al.
APL MATERIALS (2016)
A Critical Analysis of the Conventionally Employed Creep Lifing Methods
Zakaria Abdallah et al.
MATERIALS (2014)
Creep and Creep-fatigue Behaviour of 316 Stainless Steel
Stefan Holmstrom et al.
6TH INTERNATIONAL CONFERENCE ON CREEP, FATIGUE AND CREEP-FATIGUE INTERACTION (2013)
Comparison of creep rupture behaviour of type 316L(N) austenitic stainless steel joints welded by TIG and activated TIG welding processes
T. Sakthivel et al.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2011)
Analysis of creep rates of tempered martensitic 9%Cr steel based on microstructure evolution
Fujio Abe
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING (2009)
Acquisition of long-term creep data and knowledge for new applications
Koichi Yagi
INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING (2008)
Microstructure and long-term creep properties of 9-12% Cr steels
J. Hald
INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING (2008)
Ferritic/martensitic steels for next-generation reactors
R. L. Klueh et al.
JOURNAL OF NUCLEAR MATERIALS (2007)
Some chemical and microstructural factors influencing creep cavitation resistance of austenitic stainless steels
K. Laha et al.
PHILOSOPHICAL MAGAZINE (2007)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
Additive logistic regression: A statistical view of boosting
J Friedman et al.
ANNALS OF STATISTICS (2000)
Share Paper
